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R Software Module: /rwasp_decompose.wasp (opens new window with default values)

Title produced by software: Classical Decomposition

Date of computation: Wed, 13 Jan 2010 12:47:10 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Jan/13/t1263412237rm3c0yfuqgr38ny.htm/, Retrieved Wed, 13 Jan 2010 20:50:43 +0100

BibTeX entries for LaTeX users:

@Manual{KEY,
    author = {{YOUR NAME}},
    publisher = {Office for Research Development and Education},
    title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Jan/13/t1263412237rm3c0yfuqgr38ny.htm/},
    year = {2010},
}
@Manual{R,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Development Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2010},
    note = {{ISBN} 3-900051-07-0},
    url = {http://www.R-project.org},
}

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

KDGP2W52

Dataseries X:

» Textbox « » Textfile « » CSV «

104,28 104,33 104,46 104,46 104,5 104,61 104,66 104,66 105,03 105,32 105,52 105,67 105,71 105,81 106 106,02 106,19 106,22 106,34 106,42 106,84 107,23 107,42 107,63 107,69 107,81 107,92 108,06 108,21 108,44 108,55 108,66 109,23 109,7 109,94 110,13 110,39 110,46 110,67 110,89 110,98 111,12 111,33 111,43 111,87 112,22 112,47 112,64 112,84 113,03 113,09 113,27 113,44 113,51 113,66 113,62 114,01 114,55 114,77 114,87 115,11 115,09 115,24 115,27 115,41 115,59 115,6 115,68 116,2 116,55 116,73 117,04 117,12 117,28 117,48 117,66 117,92 118,12 118,17 118,39

Output produced by software:

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	104.28	NA	NA	0.165923611111111	NA
2	104.33	NA	NA	0.0749236111111083	NA
3	104.46	NA	NA	0.0340069444444448	NA
4	104.46	NA	NA	-0.0346597222222276	NA
5	104.5	NA	NA	-0.0776597222222198	NA
6	104.61	NA	NA	-0.135826388888882	NA
7	104.66	104.645590277778	104.85125	-0.20565972222222	0.0144097222222541
8	104.66	104.642173611111	104.9725	-0.330326388888884	0.0178263888888921
9	105.03	105.044756944444	105.098333333333	-0.0535763888888856	-0.0147569444444429
10	105.32	105.401256944444	105.2275	0.173756944444443	-0.0812569444444478
11	105.52	105.557923611111	105.362916666667	0.195006944444440	-0.0379236111111112
12	105.67	105.694506944444	105.500416666667	0.194090277777772	-0.0245069444444255
13	105.71	105.803423611111	105.6375	0.165923611111111	-0.0934236111111062
14	105.81	105.855756944444	105.780833333333	0.0749236111111083	-0.0457569444444346
15	106	105.963590277778	105.929583333333	0.0340069444444448	0.0364097222222313
16	106.02	106.049923611111	106.084583333333	-0.0346597222222276	-0.0299236111111156
17	106.19	106.165673611111	106.243333333333	-0.0776597222222198	0.0243263888889231
18	106.22	106.268340277778	106.404166666667	-0.135826388888882	-0.0483402777777684
19	106.34	106.362673611111	106.568333333333	-0.20565972222222	-0.0226736111110881
20	106.42	106.403840277778	106.734166666667	-0.330326388888884	0.0161597222222269
21	106.84	106.843923611111	106.8975	-0.0535763888888856	-0.00392361111111938
22	107.23	107.236256944444	107.0625	0.173756944444443	-0.00625694444445912
23	107.42	107.426673611111	107.231666666667	0.195006944444440	-0.0066736111111112
24	107.63	107.602423611111	107.408333333333	0.194090277777772	0.0275763888888889
25	107.69	107.758840277778	107.592916666667	0.165923611111111	-0.068840277777781
26	107.81	107.853256944444	107.778333333333	0.0749236111111083	-0.0432569444444368
27	107.92	108.005256944444	107.97125	0.0340069444444448	-0.0852569444444384
28	108.06	108.139090277778	108.17375	-0.0346597222222276	-0.0790902777777518
29	108.21	108.304006944444	108.381666666667	-0.0776597222222198	-0.0940069444444305
30	108.44	108.455006944444	108.590833333333	-0.135826388888882	-0.0150069444444227
31	108.55	108.601840277778	108.8075	-0.20565972222222	-0.0518402777777567
32	108.66	108.700090277778	109.030416666667	-0.330326388888884	-0.040090277777793
33	109.23	109.201840277778	109.255416666667	-0.0535763888888856	0.0281597222222416
34	109.7	109.661673611111	109.487916666667	0.173756944444443	0.0383263888889047
35	109.94	109.916256944444	109.72125	0.195006944444440	0.0237430555555562
36	110.13	110.142423611111	109.948333333333	0.194090277777772	-0.0124236111111031
37	110.39	110.341756944444	110.175833333333	0.165923611111111	0.0482430555555595
38	110.46	110.482006944444	110.407083333333	0.0749236111111083	-0.0220069444444420
39	110.67	110.666506944444	110.6325	0.0340069444444448	0.00349305555556612
40	110.89	110.812840277778	110.8475	-0.0346597222222276	0.077159722222234
41	110.98	110.980256944444	111.057916666667	-0.0776597222222198	-0.000256944444430474
42	111.12	111.132090277778	111.267916666667	-0.135826388888882	-0.0120902777777587
43	111.33	111.268923611111	111.474583333333	-0.20565972222222	0.0610763888888926
44	111.43	111.353423611111	111.68375	-0.330326388888884	0.0765763888888813
45	111.87	111.838090277778	111.891666666667	-0.0535763888888856	0.031909722222224
46	112.22	112.265423611111	112.091666666667	0.173756944444443	-0.0454236111111328
47	112.47	112.488340277778	112.293333333333	0.195006944444440	-0.0183402777777957
48	112.64	112.689506944444	112.495416666667	0.194090277777772	-0.0495069444444596
49	112.84	112.858006944444	112.692083333333	0.165923611111111	-0.0180069444444371
50	113.03	112.955340277778	112.880416666667	0.0749236111111083	0.0746597222222363
51	113.09	113.094840277778	113.060833333333	0.0340069444444448	-0.00484027777777385
52	113.27	113.212423611111	113.247083333333	-0.0346597222222276	0.0575763888888901
53	113.44	113.362340277778	113.44	-0.0776597222222198	0.077659722222208
54	113.51	113.492923611111	113.62875	-0.135826388888882	0.0170763888888956
55	113.66	113.610590277778	113.81625	-0.20565972222222	0.0494097222222223
56	113.62	113.666340277778	113.996666666667	-0.330326388888884	-0.0463402777777731
57	114.01	114.118506944444	114.172083333333	-0.0535763888888856	-0.108506944444429
58	114.55	114.518756944444	114.345	0.173756944444443	0.0312430555555636
59	114.77	114.705423611111	114.510416666667	0.195006944444440	0.064576388888895
60	114.87	114.873256944444	114.679166666667	0.194090277777772	-0.0032569444444448
61	115.11	115.012590277778	114.846666666667	0.165923611111111	0.097409722222224
62	115.09	115.088256944444	115.013333333333	0.0749236111111083	0.00174305555556487
63	115.24	115.224423611111	115.190416666667	0.0340069444444448	0.0155763888888885
64	115.27	115.330340277778	115.365	-0.0346597222222276	-0.060340277777783
65	115.41	115.452340277778	115.53	-0.0776597222222198	-0.0423402777777966
66	115.59	115.566256944444	115.702083333333	-0.135826388888882	0.023743055555542
67	115.6	115.670590277778	115.87625	-0.20565972222222	-0.0705902777777965
68	115.68	115.720923611111	116.05125	-0.330326388888884	-0.0409236111111113
69	116.2	116.182256944444	116.235833333333	-0.0535763888888856	0.0177430555555418
70	116.55	116.602506944444	116.42875	0.173756944444443	-0.0525069444444455
71	116.73	116.827923611111	116.632916666667	0.195006944444440	-0.0979236111110993
72	117.04	117.037006944444	116.842916666667	0.194090277777772	0.00299305555556373
73	117.12	NA	117.055416666667	NA	NA
74	117.28	NA	117.275416666667	NA	NA
75	117.48	NA	NA	NA	NA
76	117.66	NA	NA	NA	NA
77	117.92	NA	NA	NA	NA
78	118.12	NA	NA	NA	NA
79	118.17	NA	NA	NA	NA
80	118.39	NA	NA	NA	NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Jan/13/t1263412237rm3c0yfuqgr38ny/1ia0k1263412027.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/13/t1263412237rm3c0yfuqgr38ny/1ia0k1263412027.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/13/t1263412237rm3c0yfuqgr38ny/25bca1263412027.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/13/t1263412237rm3c0yfuqgr38ny/25bca1263412027.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/13/t1263412237rm3c0yfuqgr38ny/3zyhs1263412027.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/13/t1263412237rm3c0yfuqgr38ny/3zyhs1263412027.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/13/t1263412237rm3c0yfuqgr38ny/4w68o1263412027.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/13/t1263412237rm3c0yfuqgr38ny/4w68o1263412027.ps (open in new window)

Parameters (Session):

par1 = additive ; par2 = 12 ;

Parameters (R input):

par1 = additive ; par2 = 12 ;

R code (references can be found in the software module):

par2 <- as.numeric(par2)

x <- ts(x,freq=par2)

m <- decompose(x,type=par1)

m$figure

bitmap(file='test1.png')

plot(m)

dev.off()

mylagmax <- length(x)/2

bitmap(file='test2.png')

op <- par(mfrow = c(2,2))

acf(as.numeric(x),lag.max = mylagmax,main='Observed')

acf(as.numeric(m$trend),na.action=na.pass,lag.max = mylagmax,main='Trend')

acf(as.numeric(m$seasonal),na.action=na.pass,lag.max = mylagmax,main='Seasonal')

acf(as.numeric(m$random),na.action=na.pass,lag.max = mylagmax,main='Random')

par(op)

dev.off()

bitmap(file='test3.png')

op <- par(mfrow = c(2,2))

spectrum(as.numeric(x),main='Observed')

spectrum(as.numeric(m$trend[!is.na(m$trend)]),main='Trend')

spectrum(as.numeric(m$seasonal[!is.na(m$seasonal)]),main='Seasonal')

spectrum(as.numeric(m$random[!is.na(m$random)]),main='Random')

par(op)

dev.off()

bitmap(file='test4.png')

op <- par(mfrow = c(2,2))

cpgram(as.numeric(x),main='Observed')

cpgram(as.numeric(m$trend[!is.na(m$trend)]),main='Trend')

cpgram(as.numeric(m$seasonal[!is.na(m$seasonal)]),main='Seasonal')

cpgram(as.numeric(m$random[!is.na(m$random)]),main='Random')

par(op)

dev.off()

load(file='createtable')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,'Classical Decomposition by Moving Averages',6,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'t',header=TRUE)

a<-table.element(a,'Observations',header=TRUE)

a<-table.element(a,'Fit',header=TRUE)

a<-table.element(a,'Trend',header=TRUE)

a<-table.element(a,'Seasonal',header=TRUE)

a<-table.element(a,'Random',header=TRUE)

a<-table.row.end(a)

for (i in 1:length(m$trend)) {

a<-table.row.start(a)

a<-table.element(a,i,header=TRUE)

a<-table.element(a,x[i])

if (par1 == 'additive') a<-table.element(a,m$trend[i]+m$seasonal[i]) else a<-table.element(a,m$trend[i]*m$seasonal[i])

a<-table.element(a,m$trend[i])

a<-table.element(a,m$seasonal[i])

a<-table.element(a,m$random[i])

a<-table.row.end(a)

}

a<-table.end(a)

table.save(a,file='mytable.tab')

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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